D-optimal designs for multiarm trials with dropouts.

Multiarm trials with follow-up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables. Our framework is illustrated through redesigning a clinical trial on Alzheimer's disease, whereby the benefits of our designs compared with standard designs are demonstrated through simulations.Grant codes MR∕N028171∕ and MC_UP_1302∕

Digital simulation of dominant eddies of a co-flowing jet

This paper described a method for virtual construction of turbulent jet flow field from a limited set of experimental data. The objective is for jet visualization enhancement in near-field plume models. The method is based on the proper orthogonal decomposition (POD), which is an efficient tool to capture the turbulent field based on principal components. A set of experimental flow images at a number of cross-sections of a coflowing jet is obtained in the laboratory. Four hundred instantaneous flow images are taken at each jet section and are subjected to POD analysis. The first 60 principal modes are used in the reconstruction of jet turbulence via a low dimensional generation of jet images. The inclusion of additional modes is shown to produce little improvement on the visual appearance of the reconstructed jet images. Linear interpolation is attempted to generate jet images at an intermediate jet section between two experimental stations. Jet flow field on a central longitudinal section of the jet are assembled from the reconstructed flow images at all jet sections. The resulting development of concentration jet width is shown to agree well with the analytical solution.postprintThe 16th IAHR-APD 2008 Congress and 3rd Symposium of IAHR-ISHS, Nanjing, China, 20-23 October 2008. In Advances in Water Resources & Hydraulic Engineering, 2009, v. 2, p. 618-62

Advancing 21st Century Competencies in Hong Kong

This paper is one of five case studies that accompany the Asia Society reportpublished_or_final_versio

Designing economic evaluations to facilitate optimal decisions: The need to avoid bias

Editorial Lette

EUS could detect ascites missed by CT scan [4]

published_or_final_versio

Optimal design for experiments with possibly incomplete observations

Missing responses occur in many industrial or medical experiments, for example in clinical trials where slow acting treatments are assessed. Finding efficient designs for such experiments can be problematic since it is not known at the design stage which observations will be missing. The design literature mainly focuses on assessing robustness of designs for missing data scenarios, rather than finding designs which are optimal in this situation. Imhof, Song and Wong (2002) propose a framework for design search, based on the expected information matrix. We develop a new approach which includes Imhof, Song and Wong (2002)'s method as special case and justifies its use retrospectively. Our method is illustrated through a simulation study based on real data from an Alzheimer's disease trial

Optimal design when outcome values are not missing at random

The presence of missing values complicates statistical analyses. In design of experiments, missing values are particularly problematic when constructing optimal designs, as it is not known which values are missing at the design stage. When data are missing at random it is possible to incorporate this information into the optimality criterion that is used to find designs; Imhof, Song and Wong (2002) develop such a framework. However, when data are not missing at random this framework can lead to inefficient designs. We investigate and address the specific challenges that not missing at random values present when finding optimal designs for linear regression models. We show that the optimality criteria will depend on model parameters that traditionally do not affect the design, such as regression coefficients and the residual variance. We also develop a framework that improves efficiency of designs over those found assuming values are missing at random

Sudden cardiac death: Prevention and treatment

Sudden cardiac death remains a major health issue in western countries as well as in Hong Kong. Despite increasing knowledge of the mechanisms and risk factors of sudden cardiac death, methods for identifying high-risk candidates and predicting the efficacy of measures to prevent sudden cardiac death are still inadequate. A significant proportion of patients have known heart disease but are generally considered to be at low risk for this event. More efforts are needed to improve the success rate of out-of-hospital resuscitation through better warning systems, the use of amiodarone for refractory arrhythmias, and the widespread availability of automated defibrillation devices to allow early defibrillation. It is likely that these measures could increase the number of survivors following cardiac arrest. In survivors of sudden cardiac death episodes, treatment of the underlying cardiac disease, especially early revascularisation for myocardial ischaemia, is required. In the majority of patients, implantation of an implantable cardioverter defibrillator, with or without the use of an anti-arrhythmic drug such as amiodarone, would then be used to maintain survival. Furthermore, for individuals at significant risk of sudden cardiac death, primary prevention of sudden cardiac death through the placement of an implantable cardioverter defibrillator is increasingly being used.published_or_final_versio

Effect of case management on neonatal mortality due to sepsis and pneumonia.

BACKGROUND: Each year almost one million newborns die from infections, mostly in low-income countries. Timely case management would save many lives but the relative mortality effect of varying strategies is unknown. We have estimated the effect of providing oral, or injectable antibiotics at home or in first-level facilities, and of in-patient hospital care on neonatal mortality from pneumonia and sepsis for use in the Lives Saved Tool (LiST). METHODS: We conducted systematic searches of multiple databases to identify relevant studies with mortality data. Standardized abstraction tables were used and study quality assessed by adapted GRADE criteria. Meta-analyses were undertaken where appropriate. For interventions with biological plausibility but low quality evidence, a Delphi process was undertaken to estimate effectiveness. RESULTS: Searches of 2876 titles identified 7 studies. Among these, 4 evaluated oral antibiotics for neonatal pneumonia in non-randomised, concurrently controlled designs. Meta-analysis suggested reductions in all-cause neonatal mortality (RR 0.75 95% CI 0.64- 0.89; 4 studies) and neonatal pneumonia-specific mortality (RR 0.58 95% CI 0.41- 0.82; 3 studies). Two studies (1 RCT, 1 observational study), evaluated community-based neonatal care packages including injectable antibiotics and reported mortality reductions of 44% (RR = 0.56, 95% CI 0.41-0.77) and 34% (RR = 0.66, 95% CI 0.47-0.93), but the interpretation of these results is complicated by co-interventions. A third, clinic-based, study reported a case-fatality ratio of 3.3% among neonates treated with injectable antibiotics as outpatients. No studies were identified evaluating injectable antibiotics alone for neonatal pneumonia. Delphi consensus (median from 20 respondents) effects on sepsis-specific mortality were 30% reduction for oral antibiotics, 65% for injectable antibiotics and 75% for injectable antibiotics on pneumonia-specific mortality. No trials were identified assessing effect of hospital management for neonatal infections and Delphi consensus suggested 80%, and 90% reductions for sepsis and pneumonia-specific mortality respectively. CONCLUSION: Oral antibiotics administered in the community are effective for neonatal pneumonia mortality reduction based on a meta-analysis, but expert opinion suggests much higher impact from injectable antibiotics in the community or primary care level and even higher for facility-based care. Despite feasibility and low cost, these interventions are not widely available in many low income countries. FUNDING: This work was supported by the Bill & Melinda Gates Foundation through a grant to the US Fund for UNICEF, and to Saving Newborn Lives Save the Children, through Save the Children US